Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology

Fernández, Juan Carlos Fernández; Hervás, César; Martínez-Estudillo, Francisco José; Gutiérrez, Pedro Antonio

doi:10.1016/j.asoc.2009.12.013

Cited by 24 publications

(7 citation statements)

References 57 publications

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“…The idea behind combining DE and ANNs is based on the difficulty of determining an optimal ANN, a problem that is influenced by the characteristics of the system studied. One of the major advantages of this combination is that it relies on the ability to escape a local optimum, making it robust and adaptable to changing environments [29,30]. The combination of an evolutionary algorithm such as DE with ANNs is known as neuro-evolution and depending on the characteristics evolved, three main classes are encountered: (i) evolving weights; (ii) evolving architecture; and (iii) evolving both simultaneously [31].…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Artificial Neural Networks for Predicting Hydrogen Production in Catalytic Dry Reforming: A Systematic Review

Drăgoi

Almomani

et al. 2021

Energies

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Dry reforming of hydrocarbons, alcohols, and biological compounds is one of the most promising and effective avenues to increase hydrogen (H2) production. Catalytic dry reforming is used to facilitate the reforming process. The most popular catalysts for dry reforming are Ni-based catalysts. Due to their inactivation at high temperatures, these catalysts need to use metal supports, which have received special attention from researchers in recent years. Due to the existence of a wide range of metal supports and the need for accurate detection of higher H2 production, in this study, a systematic review and meta-analysis using ANNs were conducted to assess the hydrogen production by various catalysts in the dry reforming process. The Scopus, Embase, and Web of Science databases were investigated to retrieve the related articles from 1 January 2000 until 20 January 2021. Forty-seven articles containing 100 studies were included. To determine optimal models for three target factors (hydrocarbon conversion, hydrogen yield, and stability test time), artificial neural networks (ANNs) combined with differential evolution (DE) were applied. The best models obtained had an average relative error for the testing data of 0.52% for conversion, 3.36% for stability, and 0.03% for yield. These small differences between experimental results and predictions indicate a good generalization capability.

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Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Artificial Neural Networks for Predicting Hydrogen Production in Catalytic Dry Reforming: A Systematic Review

Drăgoi

Almomani

et al. 2021

Energies

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“…Memetic variants of DE were used to solve prediction problems in medicine and biology [127,122]. Cartesian genetic programming was used by several authors to efficiently encode evolvable ANN [137,136,162].…”

Section: Hybrid Ann+easmentioning

confidence: 99%

Adaptation and Hybridization in Nature-Inspired Algorithms

Fister

Strnad

Yang³

2015

Adaptation, Learning, and Optimization

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Abstract. The aim of this chapter is to familiarize readers with the basics of adaptation and hybridization in nature-inspired algorithms as necessary for understanding the main contents of this book. Adaptation is a metaphor for flexible autonomous systems that respond to external changing factors (mostly environmental) by adapting their wellestablished behavior. Adaptation emerges in practically all areas of human activities as well. Such adaptation mechanisms can be used as a general problem-solving approach, though it may suffer from a lack of problem-specific knowledge. To solve specific problems with additional improvements of possible performance, hybridization can be used in order to incorporate a problem-specific knowledge from a problem domain. In order to discuss relevant issues as general as possible, the classification of problems is identified at first. Additionally, we focus on the biological foundations of adaptation that constitute the basis for the formulation of nature-inspired algorithms. This book highlights three types of inspirations from nature: the human brain, Darwinian natural selection, and the behavior of social living insects (e.g., ants, bees, etc.) and animals (e.g., swarm of birds, shoals of fish, etc.), which influence the development of artificial neural networks. evolutionary algorithms, and swarm intelligence, respectively. The mentioned algorithms that can be placed under the umbrella of computational intelligence are described from the viewpoint of adaptation and hybridization so as to show that these mechanisms are simple to develop and yet very efficient. Finally, a brief review of recent developed applications is presented.

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“…The review by Zhang [28], which provides a summary of the most important advances in classification with ANNs, makes it clear that the advantages of neural networks lie in different aspects: their capability to adapt themselves to the data without any explicit specification of functional or distributional form for the underlying model; they are universal functional approximators; they represent nonlinear and flexible solutions for modeling real world complex relationships; and, finally, they are able to provide a basis for establishing classification rules and performing statistical analysis. On the other hand, different neuro-evolutionary approaches have been successfully applied to a variety of benchmark problems and real-world classification tasks [29,30,31,32,33,3]. Our neuro-evolutionary algorithm, too, has been already tested and applied with success to several realworld problems, showing how such an approach can be useful in different classification problems, like financial time series modeling [34], automated trading strategy optimization [24,35], incipient fault diagnosis in electrical drives [36], automated diagnosis of skin diseases [37], brain-wave analysis [38], etc.…”

Section: Neuro-evolutionary Classifier Systemsmentioning

confidence: 99%

SimBa: A novel similarity-based crossover for neuro-evolution

Dragoni¹,

Azzini²,

Tettamanzi³

2014

Neurocomputing

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International audienceThis work presents the SimBa (for Similarity-Based) crossover, a novel crossover operator specifically designed for the evolutionary optimization of neural network topologies that aims at overcoming one of the major problems generally related to the crossover operator, known as the permutation problem. The SimBa crossover starts by looking for a local similarity between two individuals selected from the population. The contribution of each neuron of the layer selected for the crossover is computed, and the neurons of each layer are reordered according to their contribution. Then, each neuron of the layer in the first individual is associated with the most similar neuron of the layer in the other individual, and the neurons of the layer of the second individual are re-ranked by considering the associations with the neurons of the first one. Finally, the neurons above a randomly selected cut-point are swapped to generate the offspring of the selected individuals. An approach exploiting this operator has been implemented and applied to six well-known benchmark classification problems. The experimental results, compared to those obtained by other techniques, show how this new crossover operator can help to produce compact neural networks with satisfactory generalization capability and accuracy

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Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology

Cited by 24 publications

References 57 publications

Artificial Neural Networks for Predicting Hydrogen Production in Catalytic Dry Reforming: A Systematic Review

Artificial Neural Networks for Predicting Hydrogen Production in Catalytic Dry Reforming: A Systematic Review

Adaptation and Hybridization in Nature-Inspired Algorithms

SimBa: A novel similarity-based crossover for neuro-evolution

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